La Palma 2021 Erruption data gathering and visualization

Importing libraries

from datetime import datetime as dt
from datetime import timedelta

import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px


from SeismicPortal.dl_quakes import CANARY_BOX, SeismicPortal

# sns.set_theme()
# sns.set(style="darkgrid")
# plt.rcParams['figure.figsize'] = [30, 15]

Downloading latest eartquake data for the Canary Islands

sp = SeismicPortal()
# results = sp.download_earthquakes(start_time=dt.now() - timedelta(days=14), **CANARY_BOX)
results = sp.download_earthquakes(start_time=dt.now() - timedelta(days=60), **CANARY_BOX)

1905 earthquakes downloaded.

master_df = pd.DataFrame([q.get("properties") for q in results])

# Tranform data

# Remove timezone info to remove numpy deprecation warning
master_df["time"] = master_df["time"].apply(lambda x: x.strip("Z"))
master_df["lastupdate"] = master_df["lastupdate"].apply(lambda x: x.strip("Z"))

master_df["lastupdate"] = pd.to_datetime(master_df["lastupdate"])
master_df["time"] = pd.to_datetime(master_df["time"])
master_df["date"] = master_df["time"].dt.date

# Enhance data

# Create magnitude bins

m_bins = [
    pd.Interval(left=0, right=2),
    pd.Interval(left=2, right=3),
    pd.Interval(left=3, right=4),
    pd.Interval(left=4, right=5),
    pd.Interval(left=5, right=float("inf"))
]

# m_bins_small = [
#     pd.Interval(left=-float("inf"), right=3),
#     pd.Interval(left=3, right=float("inf")),
# ]

magnitude_ranges = []
for index, row in master_df.iterrows():
    for r in m_bins:
        if row["mag"] in r:
            magnitude_ranges.append(r)

master_df['mag_range'] = magnitude_ranges

# Auto create bins
# master_df['mag_range'] = pd.cut(master_df['mag'], bins = 5)

# Add mean magnitude and depth per day

master_df = pd.merge(master_df, master_df.groupby("date").mean().round(decimals=1)[["mag", "depth"]], on="date", suffixes=("", "_mean"), how="right")

master_df = pd.merge(master_df, master_df.groupby("date").size().reset_index(name='daily_eq'), how="left", on=["date"])


master_df = master_df.sort_values("time", ascending=False, ignore_index=True)
master_df

	lastupdate	magtype	evtype	lon	auth	lat	depth	unid	mag	time	source_id	source_catalog	flynn_region	date	mag_range	mag_mean	depth_mean	daily_eq
0	2021-10-10 15:59:00	ml	ke	-17.84	MDD	28.57	10.0	20211010_0000241	2.7	2021-10-10 15:55:46.700	1046671	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-10-10	(2, 3]	2.8	19.2	92
1	2021-10-10 15:56:00	ml	ke	-17.81	MDD	28.59	33.0	20211010_0000240	2.6	2021-10-10 15:49:20.400	1046670	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-10-10	(2, 3]	2.8	19.2	92
2	2021-10-10 15:49:00	ml	ke	-17.84	MDD	28.58	8.0	20211010_0000238	2.4	2021-10-10 15:39:40.100	1046667	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-10-10	(2, 3]	2.8	19.2	92
3	2021-10-10 15:28:00	ml	ke	-17.81	MDD	28.59	35.0	20211010_0000231	3.4	2021-10-10 15:24:19.300	1046657	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-10-10	(3, 4]	2.8	19.2	92
4	2021-10-10 15:25:00	ml	ke	-17.84	MDD	28.56	11.0	20211010_0000230	2.8	2021-10-10 15:21:04.400	1046656	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-10-10	(2, 3]	2.8	19.2	92
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1900	2021-08-15 02:04:00	ml	ke	-16.68	MDD	28.23	20.0	20210815_0000014	1.6	2021-08-15 01:59:47.300	1023661	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-08-15	(0, 2]	2.0	16.0	3
1901	2021-08-14 23:29:00	ml	ke	-16.32	MDD	28.30	12.0	20210814_0000325	1.6	2021-08-14 23:17:43.400	1023602	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-08-14	(0, 2]	1.6	12.0	1
1902	2021-08-12 21:10:00	ml	ke	-16.90	MDD	28.38	8.0	20210812_0000243	1.5	2021-08-12 20:43:56.200	1022648	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-08-12	(0, 2]	1.6	15.0	2
1903	2021-08-12 05:41:00	ml	ke	-16.25	MDD	28.16	22.0	20210812_0000062	1.8	2021-08-12 05:18:56.700	1022382	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-08-12	(0, 2]	1.6	15.0	2
1904	2021-08-11 17:44:00	ml	ke	-16.26	MDD	28.13	35.0	20210811_0000155	2.1	2021-08-11 17:37:33.100	1022199	EMSC-RTS	CANARY ISLANDS, SPAIN REGION	2021-08-11	(2, 3]	2.1	35.0	1

1905 rows × 18 columns

Visualization of data

df = master_df[master_df["time"] >= dt(2021, 9, 1)]


c_map = {
    pd.Interval(left=0, right=2): "rgb(255, 142, 37)",
    pd.Interval(left=2, right=3): "rgb(248, 67, 45)",
    pd.Interval(left=3, right=4): "rgb(217, 13, 57)",
    pd.Interval(left=4, right=5): "rgb(151, 2, 61)",
    pd.Interval(left=5, right=float("inf")): "rgb(49, 49, 49)",
}

# fig = px.histogram(df.sort_values("mag_range"), x="date", color="mag_range", barmode="group", color_discrete_sequence=px.colors.cyclical.IceFire)
fig = px.histogram(
    df.sort_values("mag_range"),
    x="date",
    color="mag_range", 
    barmode="group",
    color_discrete_map=c_map,
)
fig.show()

df = master_df[master_df["time"] >= dt(2021, 9, 28)]


fig = px.scatter(df, x="time", y=-df["depth"], color="mag", size=df["mag"] ** 2 , size_max=15, color_discrete_sequence=px.colors.cyclical.IceFire)
fig.show()

df = master_df[master_df["date"] < master_df.iloc[0].date]

fig = px.line(df, x="date", y="daily_eq")

fig.show()

df = master_df[master_df["time"] >= dt.now() - timedelta(days=45)]

fig = px.histogram(df, x="date", color="mag_mean", color_discrete_sequence=px.colors.sequential.thermal, nbins=46)
fig.show()

df = master_df[
    (master_df.lat <= 28.961) & 
    (master_df.lat >= 28.324) & 
    (master_df.lon <= -17.7935) & 
    (master_df.lon >= -17.9478) &
    (master_df.date > dt.now().date() - timedelta(days=14))
]

fig = px.scatter_3d(
    df, 
    x='lat',
    y='lon',
    z=-df['depth'], 
    color='mag',
    size=df['mag'] ** 2,
    labels={
        "z": "Depth (km)",
        "mag": "Magnitude",
    },
    title="3D plot of earthquakes depth"
)

fig.show()

print(f"This notebook was last updated: {dt.utcnow()} UTC")

This notebook was last updated: 2021-10-10 16:01:49.934251 UTC

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